Worked on the numpy/numpy repository to deliver a no-copy pickle optimization for transposed NumPy arrays, targeting improved serialization efficiency and reduced memory usage. The solution enabled arrays that can be transposed to a C-contiguous layout to be serialized without unnecessary memory copies, benefiting data-intensive and multiprocessing workflows. The implementation maintained compatibility with legacy pickle formats and preserved existing API behavior, ensuring seamless integration for users. Comprehensive tests were added to validate the new functionality across various array configurations. This work demonstrated proficiency in Python programming, data serialization, and advanced numpy library usage, focusing on performance and backward compatibility in scientific computing.